Semi-Supervised Few-shot Learning via Multi-Factor Clustering

Meng Yang

Abstract:

The scarcity of labeled data and the problem of model
overfitting have been the challenges in few-shot learning.
Recently, semi-supervised few-shot learning has been developed to obtain pseudo-labels of unlabeled samples for
expanding the support set. However, the relationship between unlabeled and labeled data is not well exploited
in generating pseudo labels, the noise of which will directly harm the model learning. In this paper, we propose a Clustering-based semi-supervised Few-Shot Learning (cluster-FSL) method to solve the above problems in
image classification. By using multi-factor collaborative
representation, a novel Multi-Factor Clustering (MFC) is
designed to fuse the information of few-shot data distribution, which can generate soft and hard pseudo-labels for
unlabeled samples based on labeled data. And we exploit
the pseudo labels of unlabeled samples by MFC to expand
the support set for obtaining more distribution information. Furthermore, robust data augmentation is used for
support set in the fine-tuning phase to increase the labeled
samples’ diversity. We verified the validity of the clusterFSL by comparing it with other few-shot learning methods on three popular benchmark datasets, miniImageNet,
tieredImageNet, and CUB-200-2011. The ablation experiments further demonstrate that our MFC can effectively
fuse distribution information of labeled samples and provide high-quality pseudo-labels. Our code is available at:
https://gitlab.com/smartllvlab/cluster-fsl